基于随机森林算法的冬小麦叶面积指数遥感反演研究

doi:10.3864/j.issn.0578-1752.2018.05.005

Abstract

Abstract: 【Objective】The objective of this study is to quickly and precisely monitor the growth of winter wheat by inversion of leaf area index (LAI) using random forest algorithm. Thus it could provide a guideline in crop management and mitigation, high and stable yield, agricultural insurance claims, etc.【Method】In this study, field data of canopy reflectance and LAI of winter wheat of four critical growth stages (i.e., jointing period, flag leaf period, flowering period and filling period), were collected under different treatments. The correlation coefficient (r) analysis and the importance analysis of out-of-bag data (OOB) were combined with the random forest algorithm (RF) to determine the more suitable spectral indices and the optimal number of variables for inputs, and then two LAI inversion models (|r| -RF, OOB-RF) were constructed and validated with independent data-sets. Further, the proposed LAI inversion model was applied to the (unmanned aerial vehicle) UAV remote sensing platform to evaluate its performance and reliability for monitoring LAI of winter wheat.【Result】The results showed that the best accuracy of |r|-RF and OOB-RF inversion models was achieved when the top five spectral indices in the correlation and the top two spectral indices in the importance were used as input variables, respectively. The coefficients of determination (R²) of |r|-RF and OOB-RF models during LAI validation were 0.805 and 0.899, and the root mean square errors (RMSE) were 0.431 and 0.307, respectively, which indicated that both |r|-RF and OOB-RF models could well estimate LAI of winter wheat, while the accuracy of the latter was much higher. The retrieved LAI from the UAV hyperspectral images using the OOB-RF model was in well agreement with the ground measured values, with R²=0.761, RMSE=0.320, and the range of estimated values (i.e., 1.02-6.41) also consistent with that actually measured (i.e., 1.29-6.81).【Conclusion】The OOB-RF model constructed in this study not only has high retrieval accuracy, but also can be used to extract high-precision winter wheat LAI information from UAV hyperspectral remote sensing platform.

Key words: unmanned aerial vehicle (UAV), hyperspectral, leaf area index (LAI), random forest algorithm, winter wheat

ZHANG ChunLan, YANG GuiJun, LI HeLi, TANG FuQuan, LIU Chang, ZHANG LiYan. Remote Sensing Inversion of Leaf Area Index of Winter Wheat Based on Random Forest Algorithm[J].Scientia Agricultura Sinica, 2018, 51(5): 855-867.

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Remote Sensing Inversion of Leaf Area Index of Winter Wheat Based on Random Forest Algorithm

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